(1)ProposalLowAccuracyDataVer03.ipynb

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  "metadata": {
    "colab": {
      "name": "ProposalLowAccuracyDataVer01.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/KProj/blob/master/(1)ProposalLowAccuracyDataVer03.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "FgwWyMb6iCkO",
        "colab_type": "code",
        "outputId": "63d82221-0765-49b3-acdb-7bfe418e8a47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import gzip\n",
        "%matplotlib inline\n",
        "from keras.models import Model\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.layers import Input,Dense,Flatten,Dropout,merge,Reshape,Conv2D,MaxPooling2D,UpSampling2D,Conv2DTranspose\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.models import Model,Sequential\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import Adadelta, RMSprop,SGD,Adam\n",
        "from keras import regularizers\n",
        "from keras import backend as K\n",
        "from keras.utils import to_categorical\n",
        "from keras.datasets import cifar10\n",
        "from sklearn import preprocessing\n",
        "from keras import losses"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Mqm8mJJu0AU4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "txdR9fEabt2P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.models import model_from_json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PYdWJtFsiF1a",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.datasets import cifar10\n",
        "from keras import regularizers\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lJ9XYVEOAprv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "keras.backend.clear_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9m3ehGh2iIBA",
        "colab_type": "code",
        "outputId": "715219a9-b37f-40d9-a074-0b47f671237b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot\n",
        "from scipy.misc import toimage\n",
        "from keras.datasets import cifar10\n",
        "def show_imgs(X):\n",
        "    pyplot.figure(1)\n",
        "    k = 0\n",
        "    for i in range(0,4):\n",
        "        for j in range(0,4):\n",
        "            pyplot.subplot2grid((4,4),(i,j))\n",
        "            pyplot.imshow(toimage(X[k]))\n",
        "            k = k+1\n",
        "    # show the plot\n",
        "    pyplot.show()\n",
        " \n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "show_imgs(x_test[:16])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `toimage` is deprecated!\n",
            "`toimage` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use Pillow's ``Image.fromarray`` directly instead.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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O8NF2er2620g5XXfgeE5cX60xHA39KCJ8t8/127cBmE7G5EJIU5YlzVZM8RHA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\nvdxDuLu8zOOPPwLA0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c+VRdkF7nXkQczKNn1DO7O2do/z52VeHNvaz/PpmNHaoM0ipbMr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\nS4+xitZisVh6zP8CB+zi+GJTgIQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hVAjMdeSrloa",
        "colab_type": "code",
        "outputId": "a23c9a19-0bb2-4f5d-efe0-f4dee3b48c15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "ngfK-2Qewhtq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This function takes the entire traaining data and test data and corresponding categorical label and returns the corresponding data in class indices\n",
        "def getThreeClassesTrainTest(class_indices,train_data,train_labels,test_data,test_labels):\n",
        "  all_train_data = []\n",
        "  all_train_data_label = []\n",
        "  all_test_data = []\n",
        "  all_test_data_label = []\n",
        "  for ind in class_indices:\n",
        "    this_class_location_train =  np.where(train_labels==ind)[0] # train_labels[train_labels == ind]\n",
        "    this_class_data_train = train_data[this_class_location_train,:]\n",
        "    this_class_data_train_label = train_labels[this_class_location_train]\n",
        "    print(this_class_location_train)\n",
        "    \n",
        "    this_class_location_test = np.where(test_labels==ind)[0] #test_labels[test_labels == ind]\n",
        "    this_class_data_test = test_data[this_class_location_test,:]\n",
        "    this_class_data_test_label = test_labels[this_class_location_test]\n",
        "    \n",
        "    all_train_data.append(this_class_data_train)\n",
        "    all_test_data.append(this_class_data_test)\n",
        "    all_train_data_label.append(this_class_data_train_label)\n",
        "    all_test_data_label.append(this_class_data_test_label)\n",
        "    \n",
        "  train_data_three = all_train_data[0]\n",
        "  train_label_three = all_train_data_label[0]\n",
        "  test_data_three = all_test_data[0]\n",
        "  test_label_three = all_test_data_label[0]\n",
        "  \n",
        "  for j in range(1,len(class_indices)):\n",
        "    train_data_three = np.concatenate((train_data_three,all_train_data[j]))\n",
        "    train_label_three = np.concatenate((train_label_three,all_train_data_label[j]))\n",
        "\n",
        "    test_data_three = np.concatenate((test_data_three,all_test_data[j]))\n",
        "    test_label_three = np.concatenate((test_label_three,all_test_data_label[j]))\n",
        "  print(train_label_three.shape)\n",
        "  train_label_three = np.reshape(train_label_three,newshape=(-1,))\n",
        "  test_label_three = np.reshape(test_label_three,newshape=(-1,))\n",
        "  return train_data_three, train_label_three, test_data_three,test_label_three"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3ZJV35fkrj3O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "         "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2ekn-CkLikYJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Get only three classes of data and train the model on it"
      ]
    },
    {
      "metadata": {
        "id": "VPYv1L-uigzL",
        "colab_type": "code",
        "outputId": "b9728759-8fea-41a5-eb66-8fbb0f230197",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_data,train_three_label,test_three_data,test_three_label = getThreeClassesTrainTest([ 0,3,5 ],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[   29    30    35 ... 49941 49992 49994]\n",
            "[    9    17    21 ... 49979 49982 49983]\n",
            "[   27    40    51 ... 49964 49980 49988]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9luxTYeM3aZO",
        "colab_type": "code",
        "outputId": "449880aa-ebfa-4de3-de54-6b6ef1dbbe85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([5000.,    0.,    0.,    0.,    0.,    0., 5000.,    0.,    0.,\n",
              "        5000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PAb8f90RGrIDvJjnYvh1hWazEAKgjSV4LfB34aFX9etzzWW5V9UJVXcTgU/Sbk6zYU35J\n3gMcraqD457LGLyzqi5m8M3Ju9pp3iW3EgOw4NdNaGVo58G/Dny5qr4x7vmMUlU9A9wNbB33XJbR\npcB72/nwfcBlSf59vFMajao60m6PAt9kcGp7ya3EAPh1Ex1ob4jeAhyqqs+Mez6jkGQiyZq2/BoG\nFzr8dLyzWj5VdX1VbaiqSQZ/j++qqr8b87SWXZJz24UNJDkX2AIsyxV+Ky4AVXUSOPV1E4eA/Sv9\n6yaSfAX4T+AtSQ4n2THuOY3ApcCHGPxW+GD7uWrck1pm64C7kzzE4Bed6arq5tLIjqwFfpDkR8B9\nwLer6jvL8UQr7jJQSdJwVtwrAEnScAyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXqfwHd\n/gESZomJ0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Dni1oqIcyPai",
        "colab_type": "code",
        "outputId": "023c25ce-2f2b-408b-e012-a4afd3f9b61a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "# test_three_label.shape\n",
        "plt.hist(test_three_label)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1000.,    0.,    0.,    0.,    0.,    0., 1000.,    0.,    0.,\n",
              "        1000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qqpuXRnZkDfBwkm8BXwe+VFVfXo4TrbiXgUqSTs2KewYgSTo1BkCSOmUAJKlTBkCSOmUA\nJKlTBkCSOmUAJKlTBkCSOvW/lK9vO4tP9cMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fuM8CeuRsDzW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_three_data, train_three_label = shuffle(train_three_data, train_three_label )\n",
        "test_three_data,test_three_label = shuffle(test_three_data,test_three_label )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EshrdJ78fp7U",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mbK7iwsZx8QW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "le.fit_transform(train_three_label)\n",
        "train_three_label_cat = le.transform(train_three_label)\n",
        "test_three_label_cat = le.transform(test_three_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jYJt52bU0ooK",
        "colab_type": "code",
        "outputId": "cc36e016-d6dd-40e6-83f5-d6bf59329f53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_label_cat.shape"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "metadata": {
        "id": "r3Ydm3cykUFn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_three_label_one_hot = to_categorical(train_three_label_cat)\n",
        "test_three_label_one_hot = to_categorical(test_three_label_cat)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_O4_VLoJ6vSF",
        "colab_type": "code",
        "outputId": "33806879-20ea-4909-a20c-a8163e01136c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_label_one_hot.shape"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "metadata": {
        "id": "UIrWUPz_lGvF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# split train data and to create validation data\n",
        "train_X,valid_X,train_ground,valid_ground = train_test_split(train_three_data,\n",
        "                                                             train_three_label_one_hot,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H6AkQaz-kRh0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "duFF_pGbj2i1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x, y = 32, 32\n",
        "inChannel = 3\n",
        "input_img = Input(shape = (x, y, inChannel))\n",
        "ep = 10 \n",
        "weight_decay = 1e-4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Pt1coy0mkFrj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.001\n",
        "    if epoch > 75:\n",
        "        lrate = 0.0005\n",
        "    if epoch > 100:\n",
        "        lrate = 0.0003\n",
        "    return lrate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4IbtMUdhiscG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def cifar10Classifier(input_img):\n",
        "  \n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32\n",
        "  drop1= Dropout(.2)(pool1)\n",
        "\n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop1)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv2)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #14 x 14 x 32\n",
        "  drop2= Dropout(.3)(pool2)\n",
        "\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) #14 x 14 x 32\n",
        "  drop3= Dropout(.4)(pool3)\n",
        "\n",
        "  flat = Flatten()(drop3)\n",
        "  out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-VPIvSX1jytM",
        "colab_type": "code",
        "outputId": "f8946534-dc0a-40a2-8131-82c644d6b428",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "cell_type": "code",
      "source": [
        "model = Model(input_img, (cifar10Classifier(input_img)))\n",
        "#data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "datagen.fit(train_three_data)\n",
        " \n",
        "#training\n",
        "batch_size = 64\n",
        " \n",
        "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
        "# model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "model.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n",
        " "
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9Wi6iL1A7DxR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# model.get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2jabvkmcr1KR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mcp_save = ModelCheckpoint('weights.hdf5', save_best_only=True, monitor='val_loss', mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "T6x5OsaN9CxB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AAxjpgNk9KF3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# for l1,l2 in zip(model.layers[0:],loaded_model_high.layers[0:]):\n",
        "#     l1.set_weights(l2.get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0x-iDMcukCCT",
        "colab_type": "code",
        "outputId": "00a2da38-a563-4e20-a195-72d99c52702e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2150
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=60,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])\n",
        "#save to disk\n",
        "model_json = model.to_json()\n",
        "with open('model.json', 'w') as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights('model.h5') \n",
        " \n"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/60\n",
            "187/187 [==============================] - 12s 66ms/step - loss: 1.1527 - acc: 0.5993 - val_loss: 1.6709 - val_acc: 0.5597\n",
            "Epoch 2/60\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.8185 - acc: 0.6569 - val_loss: 0.7641 - val_acc: 0.6620\n",
            "Epoch 3/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.7057 - acc: 0.7016 - val_loss: 0.8071 - val_acc: 0.6690\n",
            "Epoch 4/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.6439 - acc: 0.7279 - val_loss: 0.6128 - val_acc: 0.7383\n",
            "Epoch 5/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.6051 - acc: 0.7500 - val_loss: 1.2258 - val_acc: 0.6437\n",
            "Epoch 6/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.5706 - acc: 0.7678 - val_loss: 0.6790 - val_acc: 0.7213\n",
            "Epoch 7/60\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.5418 - acc: 0.7843 - val_loss: 0.6041 - val_acc: 0.7630\n",
            "Epoch 8/60\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.5288 - acc: 0.7924 - val_loss: 0.5607 - val_acc: 0.7850\n",
            "Epoch 9/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.5137 - acc: 0.7979 - val_loss: 0.4902 - val_acc: 0.8097\n",
            "Epoch 10/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.5004 - acc: 0.8042 - val_loss: 0.5766 - val_acc: 0.7977\n",
            "Epoch 11/60\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.4912 - acc: 0.8107 - val_loss: 0.4871 - val_acc: 0.8110\n",
            "Epoch 12/60\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.4909 - acc: 0.8199 - val_loss: 0.7194 - val_acc: 0.7863\n",
            "Epoch 13/60\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.4720 - acc: 0.8253 - val_loss: 0.6045 - val_acc: 0.7830\n",
            "Epoch 14/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4599 - acc: 0.8315 - val_loss: 0.4438 - val_acc: 0.8357\n",
            "Epoch 15/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4514 - acc: 0.8336 - val_loss: 0.4245 - val_acc: 0.8483\n",
            "Epoch 16/60\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.4485 - acc: 0.8324 - val_loss: 0.5538 - val_acc: 0.8010\n",
            "Epoch 17/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4429 - acc: 0.8323 - val_loss: 0.6352 - val_acc: 0.7743\n",
            "Epoch 18/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4359 - acc: 0.8381 - val_loss: 0.5816 - val_acc: 0.8003\n",
            "Epoch 19/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4361 - acc: 0.8444 - val_loss: 0.4906 - val_acc: 0.8297\n",
            "Epoch 20/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.4258 - acc: 0.8478 - val_loss: 0.4593 - val_acc: 0.8423\n",
            "Epoch 21/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.4227 - acc: 0.8463 - val_loss: 0.4643 - val_acc: 0.8337\n",
            "Epoch 22/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4136 - acc: 0.8519 - val_loss: 0.5521 - val_acc: 0.7927\n",
            "Epoch 23/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4244 - acc: 0.8487 - val_loss: 0.4068 - val_acc: 0.8613\n",
            "Epoch 24/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4046 - acc: 0.8605 - val_loss: 0.4645 - val_acc: 0.8403\n",
            "Epoch 25/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4103 - acc: 0.8556 - val_loss: 0.4123 - val_acc: 0.8450\n",
            "Epoch 26/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4074 - acc: 0.8585 - val_loss: 0.4639 - val_acc: 0.8450\n",
            "Epoch 27/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4055 - acc: 0.8548 - val_loss: 0.4302 - val_acc: 0.8520\n",
            "Epoch 28/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.3931 - acc: 0.8613 - val_loss: 0.5598 - val_acc: 0.8197\n",
            "Epoch 29/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3936 - acc: 0.8662 - val_loss: 0.4401 - val_acc: 0.8473\n",
            "Epoch 30/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3858 - acc: 0.8675 - val_loss: 0.4446 - val_acc: 0.8510\n",
            "Epoch 31/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3896 - acc: 0.8678 - val_loss: 0.4343 - val_acc: 0.8507\n",
            "Epoch 32/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3903 - acc: 0.8665 - val_loss: 0.5352 - val_acc: 0.8017\n",
            "Epoch 33/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3837 - acc: 0.8692 - val_loss: 0.3931 - val_acc: 0.8667\n",
            "Epoch 34/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3944 - acc: 0.8649 - val_loss: 0.4105 - val_acc: 0.8563\n",
            "Epoch 35/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3915 - acc: 0.8671 - val_loss: 0.4576 - val_acc: 0.8447\n",
            "Epoch 36/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3909 - acc: 0.8639 - val_loss: 0.4134 - val_acc: 0.8630\n",
            "Epoch 37/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.3832 - acc: 0.8702 - val_loss: 0.4522 - val_acc: 0.8457\n",
            "Epoch 38/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3779 - acc: 0.8737 - val_loss: 0.4647 - val_acc: 0.8357\n",
            "Epoch 39/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3781 - acc: 0.8761 - val_loss: 0.5084 - val_acc: 0.8390\n",
            "Epoch 40/60\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.3654 - acc: 0.8774 - val_loss: 0.4503 - val_acc: 0.8470\n",
            "Epoch 41/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3715 - acc: 0.8758 - val_loss: 0.6285 - val_acc: 0.8030\n",
            "Epoch 42/60\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.3756 - acc: 0.8732 - val_loss: 0.3780 - val_acc: 0.8730\n",
            "Epoch 43/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3658 - acc: 0.8806 - val_loss: 0.4154 - val_acc: 0.8687\n",
            "Epoch 44/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.3690 - acc: 0.8779 - val_loss: 0.4263 - val_acc: 0.8617\n",
            "Epoch 45/60\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.3659 - acc: 0.8795 - val_loss: 0.4499 - val_acc: 0.8737\n",
            "Epoch 46/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3549 - acc: 0.8865 - val_loss: 0.4249 - val_acc: 0.8623\n",
            "Epoch 47/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3721 - acc: 0.8796 - val_loss: 0.4009 - val_acc: 0.8760\n",
            "Epoch 48/60\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.3594 - acc: 0.8849 - val_loss: 0.4583 - val_acc: 0.8607\n",
            "Epoch 49/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3685 - acc: 0.8868 - val_loss: 0.3863 - val_acc: 0.8707\n",
            "Epoch 50/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.3566 - acc: 0.8881 - val_loss: 0.4005 - val_acc: 0.8777\n",
            "Epoch 51/60\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.3549 - acc: 0.8904 - val_loss: 0.4732 - val_acc: 0.8573\n",
            "Epoch 52/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3628 - acc: 0.8827 - val_loss: 0.4015 - val_acc: 0.8773\n",
            "Epoch 53/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3556 - acc: 0.8869 - val_loss: 0.4080 - val_acc: 0.8743\n",
            "Epoch 54/60\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.3535 - acc: 0.8878 - val_loss: 0.4020 - val_acc: 0.8637\n",
            "Epoch 55/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.3527 - acc: 0.8914 - val_loss: 0.4338 - val_acc: 0.8560\n",
            "Epoch 56/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3536 - acc: 0.8890 - val_loss: 0.4998 - val_acc: 0.8467\n",
            "Epoch 57/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3461 - acc: 0.8918 - val_loss: 0.4250 - val_acc: 0.8583\n",
            "Epoch 58/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3489 - acc: 0.8918 - val_loss: 0.3931 - val_acc: 0.8783\n",
            "Epoch 59/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3535 - acc: 0.8885 - val_loss: 0.4988 - val_acc: 0.8307\n",
            "Epoch 60/60\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.3391 - acc: 0.8976 - val_loss: 0.4166 - val_acc: 0.8743\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DYZeckSZ1FA4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.load_weights('weights.hdf5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GlQJMg1YnI17",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.save('lowAccuracyModelData.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s0zPvx9oEN5_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b0a0a091-8cdd-4849-eb99-e083b5f7deae"
      },
      "cell_type": "code",
      "source": [
        "# temp_model = keras.models.load_model('lowAccuracyModelData.h5')\n",
        "# temp_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3779547094504038, 0.873]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "metadata": {
        "id": "KltF5yv2AZES",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "93c84136-f5c6-4767-c6af-2ccf74ada7fb"
      },
      "cell_type": "code",
      "source": [
        "accuracy = model.history.history['acc']\n",
        "val_accuracy = model.history.history['val_acc']\n",
        "loss = model.history.history['loss']\n",
        "val_loss = model.history.history['val_loss']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'b', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'r', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'b', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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91wWKIYmJthr6okVm9759tn3rVnj2WTjmGDj1VGjfHr7/PsuWTp1sAfHk5Nxt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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q7HrgjyLyHTAOuEi1XM+LVo6AuB/yw/s8y0W8dmAg5N+li72XDs3k59uDSnXr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\nFMTF3XEcJwX5/8LZdxVXzHsAAAABSURBVAbBW9jdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fJHowfrN1HQg",
        "colab_type": "code",
        "outputId": "b67bc210-119c-4f67-e6d9-45c1df075709",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3779547107219696, 0.873]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "metadata": {
        "id": "w4M2DfGjmT3I",
        "colab_type": "code",
        "outputId": "46c03f09-6291-416a-fd6c-e2a19982ba5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "#testing\n",
        "test_eval = model.evaluate(test_three_data, test_three_label_one_hot, verbose=0)\n",
        "print('Test loss:', test_eval[0])\n",
        "print('Test accuracy:', test_eval[1])"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.37903060110410053\n",
            "Test accuracy: 0.874666666507721\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M1_boK_fprtB",
        "colab_type": "code",
        "outputId": "bb28e1b1-0f38-4dcd-81bd-d6002e4c5c36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_data.shape"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "metadata": {
        "id": "ZuH6igT7nbza",
        "colab_type": "code",
        "outputId": "646b7ab4-08d5-469a-abba-30a65d1f361a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "# show_imgs(test_three_data[:,1])\n",
        "show_imgs(test_three_data[:16])"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `toimage` is deprecated!\n",
            "`toimage` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use Pillow's ``Image.fromarray`` directly instead.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cUbKdcGMghWTgoVsWG7gjRrlm6Aljqq15+l2nK6WHYaTMfrfD3W2XRlhrypC6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\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cE1UG2RavZW3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def getDIScoreOfLayer(layer_output, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  rho = 1e-6\n",
        "  mu = layer_output.mean(axis=0)\n",
        "#   print(mu.shape)\n",
        "  mu = mu.reshape((1,-1))\n",
        "  centered_data = layer_output - mu\n",
        "  S_bar = np.matmul(centered_data.T,centered_data)\n",
        "  # compute between class matrix\n",
        "  total_classes = true_labels.max()\n",
        "  S_B = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  S_W = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  unique_classes = np.unique(true_labels)\n",
        "  unique_classes_list = np.ndarray.tolist(unique_classes)\n",
        "  for l in unique_classes_list:\n",
        "    class_l_index = np.where(true_labels == l)\n",
        "    class_l_output = layer_output[class_l_index[0],:]\n",
        "    mu_l = class_l_output.mean(axis=0)\n",
        "    mu_l = mu_l.reshape((1,-1))\n",
        "    total_examples_this_class = class_l_index[0].shape   \n",
        "    ss = mu_l - mu\n",
        "    S_B = S_B + total_examples_this_class*np.matmul((mu_l - mu).T,(mu_l - mu))\n",
        "\n",
        "  DI_Score = np.trace(np.matmul(np.linalg.inv(S_bar + rho*np.eye(S_bar.shape[0])),S_B))\n",
        "  return DI_Score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2_q12H9HU7n-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def getDIScoreOfLayerCNNLayer(cnn_outputs, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  number_of_filters = cnn_outputs.shape[3]\n",
        "  cnn_di_scores = []\n",
        "  for filt in range(0,number_of_filters):\n",
        "    outputs_filter = cnn_outputs[:,:,:,filt]\n",
        "    outputs_filter_reshaped = np.reshape(outputs_filter, newshape=(cnn_outputs.shape[0],-1))\n",
        "    this_filter_score = getDIScoreOfLayer(outputs_filter_reshaped, true_labels)\n",
        "    cnn_di_scores.append(this_filter_score)\n",
        "  return cnn_di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mPTmOl-QERZN",
        "colab_type": "code",
        "outputId": "b4c8941f-eef5-4268-9204-2e3c0ce111be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# layer_name\n",
        "valid_X.shape"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "metadata": {
        "id": "bRQ1YTnsd6zM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# train_three_data, train_three_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pe0g2T0IA7sG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'conv2d_6'\n",
        "get_cnn1_output_function = K.function([model.layers[0].input],\n",
        "                                  [model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "cnn1_output = get_cnn1_output_function([train_three_data])[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4Yy-y5xIWdz6",
        "colab_type": "code",
        "outputId": "5e6df7f7-e5ae-46c1-9595-776409db293c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 8, 8, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "metadata": {
        "id": "26Q6Hfa8rTd6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.utils.vis_utils import plot_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EF3q_KOgYTxb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "plot_model(model, to_file=\"cifar10Architecture.png\", show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "B0Hfr4ZwB8dX",
        "colab_type": "code",
        "outputId": "186689eb-681e-4ee6-e6d2-35734439e701",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.ndarray.tolist(np.unique(test_three_label))"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "metadata": {
        "id": "pM8xfShbdHJV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# valid_ground_cat = np.argmax(train_ground, axis=1)\n",
        "# # valid_ground_cat\n",
        "# valid_ground.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jZ1IST7pBZ92",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "di_scores = getDIScoreOfLayerCNNLayer(cnn1_output, train_three_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RZnzBi-7BcfM",
        "colab_type": "code",
        "outputId": "d47065e9-6842-403f-903c-537c8fda1bce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "cell_type": "code",
      "source": [
        "y_pos  = np.arange(len(di_scores))\n",
        "plt.bar(y_pos, di_scores, align='center', alpha=0.5)\n",
        "plt.xlabel('FilterNumber')\n",
        "plt.ylabel('DI Score')\n",
        "\n",
        "print('Mean of DI SCore: ',np.mean(di_scores))\n",
        "print('STD of DI Score: ', np.std(di_scores))\n",
        "print('MAX of DI Score: ', np.max(di_scores))\n",
        "plt.title('DI score - Data - D_L, Network -N_L, Conv_6')\n",
        "plt.ylim([0,1])"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.46968247829146104\n",
            "STD of DI Score:  0.17012919206958768\n",
            "MAX of DI Score:  0.8879906467627727\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jvBPUHZoKBUnrSXrVEOd9CzBF0mRJ6wCHA3NqpvkRqZWApC1I3UlLhvh/zMzGnHaF6KCh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\nrOBQMDOzwv8HFvkwSAKN7XoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gFs0BsyhzMXk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "Xkj_8m7OpfxD"
      },
      "cell_type": "markdown",
      "source": [
        "## DI on different data\n",
        "\n",
        "1.   List item\n",
        "2.   List item\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "9c789423-23ba-478b-eeeb-64a19bd8cefe",
        "id": "JcsAbLG_pfwz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train_three_data_low,train_three_label_low,test_three_data_low,test_three_label_low = getThreeClassesTrainTest([1,8,9],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[    4     5    32 ... 49993 49998 49999]\n",
            "[    8    62    69 ... 49968 49976 49985]\n",
            "[    1     2    14 ... 49963 49971 49997]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "vA_9sjRGpfwn",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# split train data and to create validation data\n",
        "train_X_low,valid_X_low,train_ground_low,valid_ground_low = train_test_split(train_three_data_low,\n",
        "                                                             train_three_label_low,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "6939f482-af89-4f16-8050-cd1bd5d1116a",
        "id": "-zNgfVTPpfwa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'conv2d_6'\n",
        "get_cnn1_output_function_low = K.function([model.layers[0].input],\n",
        "                                  [model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "cnn1_output_low = get_cnn1_output_function_low([train_three_data_low])[0]\n",
        "cnn1_output_low.shape"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 8, 8, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "C0RAKBX5pfwR",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "di_scores_low = getDIScoreOfLayerCNNLayer(cnn1_output_low, train_three_label_low)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "dad4626d-35b5-4e26-b7ac-f7c83d2e5ad1",
        "id": "lYpxC9KMpfwD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "cell_type": "code",
      "source": [
        "y_pos  = np.arange(len(di_scores_low))\n",
        "plt.bar(y_pos, di_scores_low, align='center', alpha=0.5)\n",
        "plt.xlabel('FilterNumber')\n",
        "plt.ylabel('DI Score')\n",
        "\n",
        "print('Mean of DI SCore: ',np.mean(di_scores_low))\n",
        "print('STD of DI Score: ', np.std(di_scores_low))\n",
        "print('MAX of DI Score: ', np.max(di_scores_low))\n",
        "plt.title('DI score - Data - D_H, Network -N_L, Conv_6')\n",
        "plt.ylim([0,1])"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.25325489539602447\n",
            "STD of DI Score:  0.07981976639878438\n",
            "MAX of DI Score:  0.48132421143127047\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SNylI2g24E7giD39IUt+D+xIiYjFwEHAlcB9wfkTMknSMpEm52JXAAkn3AtcCh0XEgqEt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\nZjg+a2V/5gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "964f1ccc-2699-487a-9298-1fb1a316a3f9",
        "id": "UH9t29tspfv1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "lK3KHL2Bpfvt",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# # not working\n",
        "# mat_save = {'diScore': di_scores, 'conv2d_1': cnn1_output, 'trueLabel':train_three_label}\n",
        "# savemat('LowAccuracyConv2d_1MatrixMethod', mat_save) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Fvw5s5K8pfvk",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "PMqp4Ao3pfvg"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "jkozbZpypfvX",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "90f23b1a-a2ef-43cc-f7a8-e6d22f0c1645",
        "id": "x-H8BlZUpfuw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "sum(di_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.049834714257239"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "dc5ee49b-0567-4d1b-a541-5a8f8731c467",
        "id": "JvJ41KIfpfuc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "max(di_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6375973839423631"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "uWwVdXn0pft-",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from scipy.io import savemat\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "7GwdcjmBpfs0",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}